Decision Trees are models that make predictions by learning a hierarchy of if-then rules from data. Random Forests improve on this by combining many trees into an ensemble that is more accurate and resistant to overfitting.
In Depth
A Decision Tree is one of the most intuitive machine learning models. It learns a series of hierarchical yes/no questions about the input features and follows a path from root to leaf to reach a prediction. For example, a tree predicting loan default might first ask 'Is income above $50K?' then 'Is credit score above 700?' and so on. Each split is chosen to maximize information gain or minimize impurity. The result is a transparent, interpretable model that mirrors human decision-making logic.
The major weakness of a single decision tree is overfitting — a deep tree can memorize the training data perfectly by creating a unique path for every example, but generalizes poorly to new data. Random Forests solve this by building hundreds or thousands of decision trees, each trained on a random subset of the data and features, then aggregating their predictions (majority vote for classification, average for regression). This ensemble approach dramatically reduces variance while maintaining the expressive power of tree-based models.
Random Forests are among the most reliable 'out-of-the-box' machine learning algorithms. They handle numerical and categorical features, require minimal preprocessing, are robust to outliers, and provide feature importance scores that help interpret which variables drive predictions. Gradient Boosted Trees (XGBoost, LightGBM, CatBoost) take a different ensemble approach — building trees sequentially, where each new tree corrects the errors of the previous ones — and often achieve even higher accuracy, particularly on tabular data.
Decision Trees offer intuitive, interpretable predictions; Random Forests combine many trees into a powerful ensemble that resists overfitting — together they are the workhorses of tabular data modeling.